The use of packet-based communication networks to transport digitized streams of audio, data and video is very common. The characteristics of these networks are such that the total delay experienced by each data packet is a function of variable delays due to physical media access, relay queuing, and choice of routes in addition to fixed propagation delays. The result is that the time difference between transmitting any two packets at the source is unlikely to be the same as that observed upon their arrival at the destination. The delay variations (known as jitter) are a particular problem for a stream of multimedia packets, because the jitter can have an impact on the audiovisual quality as perceived by a human user.
Synchronization methods are used at the receiver to handle delay variations. These synchronization methods usually operate by selectively choosing to drop certain packets deemed to be late or by adding further playback delays to certain packets at the receiver. The playback delay represents delay experienced at the playback location or at any output means. Thus, the total delay experienced by any packet represents the sum of network delay and playback delay.
The synchronization methods store and track the statistical trends of the delays occurring within the network. Since the network characteristics vary with time, these statistical trends vary, and current information is necessary for the methods to be effective.
There are various prior art approaches to storing the statistical trends to keep the information current. One approach is the "full aggregation" method wherein all of the data is accumulated into a single distribution curve throughout the lifetime of the transmission. The second approach is the "flush and refresh" approach in which statistical samples are stored for a period of time and then periodically flushed and refreshed.
With the first approach, the recent information and old information are given the same weight in terms of their influence on the distribution function estimate. Therefore, it is slow to react to changes occurring in the system. With the second approach, the periodic flush results in a complete loss of historic information and can introduce boundary effects at the flush instances. Neither approach is suitable for a wide range of applications which are sensitive to total end-to-end delay (TED), which need a method that can dynamically control the delay of network multimedia streams.
As a result there is a need for a dynamic statistical method which can predict the future TED by analyzing historical and current information and can monitor, maintain, update, and store dynamic statistical trends of network delays.